Analyze how well a candidate's CV matches a job description. Watu (Swahili for "people") uses AI to score fit, identify strengths and gaps, and suggest interview questions.
git clone https://github.com/makhembu/watu
cd watu
cp .env.example .env
# Add your Gemini API key to .env
npm install
npm run dev
# Open http://localhost:3000- Paste a job description and a CV
- AI analyzes both for skill overlap, experience fit, and gaps
- Returns a score (1-10), strengths, gaps, recommendations, and interview questions
- Works without an API key (rule-based fallback)
- AI-powered analysis using Google Gemini
- Rule-based fallback — works even without an API key
- Dark mode UI — easy on the eyes
- No data storage — privacy by design
- Rate limited — 10,000 character input limit
- Export-ready — scores and analysis you can screenshot
- Next.js 15 (App Router)
- TypeScript
- Google Gemini API
- Tailwind CSS 4
- Server-side API route
# Deploy to Vercel
npx vercel --prodSet GEMINI_API_KEY in your Vercel environment variables.
Hiring in Africa often relies on manual CV screening. Watu makes the first pass instant, consistent, and bias-reduced. Built for recruiters, HR teams, and hiring managers who want a second opinion.
- PDF upload support
- Batch CV comparison
- Export reports (PDF)
- API key management UI
- Multiple AI model support